Air-conditioner based on parameter learning using artificial intelligence, cloud server, and method of operating and controlling thereof

ABSTRACT

An air conditioner includes: an indoor unit including a blowing unit to discharge air; an outdoor unit supplying compressed refrigerant to the indoor unit, to exchange heat with the air before discharge. The air conditioner also includes at least one computer memory operably connectable to at least one processor and storing instructions that, when executed, perform operations that include: generating at least one parameter in a first operation mode, which is operated only within a preset time range; controlling, based on an operation mode information, at least one of (i) the blowing unit to change a wind direction and an air volume, or (ii) the outdoor unit; and switching to a second operation mode after a time period of operation in the first operation mode. The operation mode information includes at least one result factor obtained from at least one machine-learning network.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 119 to KoreanApplication No. 10-2018-0041550, filed on Apr. 10, 2018, the entiredisclosure of which is herein incorporated by reference.

TECHNICAL FIELD

The present disclosure generally relates to an air conditioner thatoperates based on parameter learning.

BACKGROUND

An air conditioner is typically configured to provide a comfortableindoor environment to humans by discharging cold and warm air to aninterior to adjust an indoor temperature and by purifying indoor air.

SUMMARY

One general aspect of the present disclosure includes an airconditioner, including: an indoor unit including a blowing unitconfigured to discharge air. The air conditioner also includes anoutdoor unit configured to supply compressed refrigerant to the indoorunit, such that the supplied refrigerant exchanges heat with the airbefore the air is discharged from the blowing unit. The air conditioneralso includes at least one processor; and at least one computer memoryoperably connectable to the at least one processor and storinginstructions that, when executed by the at least one processor, performoperations including: generating at least one parameter in a firstoperation mode of the air conditioner, where the first operation mode isoperated only within a preset time range. The operations also includecontrolling, based on an operation mode information, at least one of (i)the blowing unit of the indoor unit to change a wind direction and anair volume of the discharged air, or (ii) the outdoor unit, where theoperation mode information includes at least one result factor that isObtained from at least one machine-learning network. The operations alsoinclude performing an operation of switching to a second operation modeafter a time period of operation in the first operation mode. Anabsolute value of a first temperature change rate during the firstoperation mode is greater than an absolute value of a second temperaturechange rate during the second operation mode. Other embodiments of thisaspect include corresponding computer systems, apparatus, and computerprograms recorded on one or more computer storage devices, eachconfigured to perform the actions of the methods.

Implementations may include one or more of the following features. Theair conditioner further including a communication unit configured tocommunicate with a cloud server, where the operations may also includewhere the at least one result factor obtained from the at least onemachine-learning network is obtained from at least one of (i) the cloudserver after the communication unit transmits the at least one parameterto the cloud server, or (ii) an embedded learning unit. The airconditioner where the operations further include: based on the airconditioner operating for cooling, maintaining a temperature during afirst period from a time point when the first operation mode ends, andsubsequently increasing the temperature compared to the temperature ofthe first period during a second period; and based on the airconditioner operating for heating, maintaining a temperature during afirst period from a time point when the first operation mode ends, andsubsequently reducing the temperature compared to the temperature of thefirst period during a second period, where the first period and thesecond period form a time interval of the second operation mode: The airconditioner where the operations further include: receiving, through aninterface unit, a control signal related to an increase or a decrease ina temperature of the air conditioner, where, based on the airconditioner operating for cooling in the second operation mode,increasing or decreasing a length of the first period or the secondperiod in inverse proportion to the increase or the decrease of thetemperature by the control signal. The air conditioner where theoperations further include: based on a difference between a currenttemperature and a target set temperature being within a temperatureband, or based on the difference being maintained for a time period inthe second operation mode, performing an operation to switch to thefirst operation mode. The air conditioner where the operations furtherinclude: performing a humidity control operation mode based on ahumidity level, which is sensed at a time of switching from the firstoperation mode to the second operation mode, exceeding a first referencethreshold criteria; and releasing the humidity control operation modebased on the humidity level reaching a second reference thresholdcriteria and being maintained for a period of time. The air conditionerwhere the operations further include: storing position informationrelated to a residing area, based on an occupant repeatedly beingdetected in a space where the air conditioner is installed; and settinga left/right wind direction of the blowing unit in the first operationmode to be directed to the residing area. The air conditioner where theoperations further include: storing position information related to anactivity area that includes the residing area, based on the occupantbeing intermittently detected; and setting the left/right wind directionof the blowing unit in the second operation mode to the activity area.The air conditioner where the operations further include: based on (i)the air conditioner starting to operate and the difference between acurrent indoor temperature and a target set temperature being greaterthan a predefined value, or (ii) the air conditioner receiving an inputsignal that instructs a driving of the first operation mode: performingan operation of switching to the first operation mode: The airconditioner where the at least one parameter includes: (i) an indoorinitial temperature related to a time at which the first operation modeis started, (ii) a target set temperature for the first operation mode,(iii) a first temperature change rate during an initial time interval ofthe first operation mode, (iv) a second temperature change rate duringthe first operation mode, and (v) a time difference between a start timeand an end time of the first operation mode. The air conditioner wherethe at least one machine-learning network includes: at least one inputlayer that has the at least one parameter as at least one input node; atleast one output layer that has the operation mode information as atleast one output node; and at least one hidden layer arranged betweenthe at least one input layer and the at least one output layer, whereweights of at least one node and at least one edge between the at leastone input node and the at least one output node are updated by alearning process of the at least one machine-learning network.Implementations of the described techniques may include hardware, amethod or process, or computer software on a computer-accessible medium.

Another general aspect includes a method of controlling an airconditioner based on machine-learning, the air conditioner including (i)an indoor unit having a blowing unit configured to discharge air, and(ii) an outdoor unit configured to supply compressed refrigerant to theindoor unit, the method including: operating the blowing unit todischarge air in a first operation mode of the air conditioner within apreset time range, where the first operation mode is operated onlywithin a preset time range. The method also includes generating at leastone parameter in the first operation mode of the air conditioner. Themethod also includes controlling, based on an operation modeinformation, at least one of (i) the blowing unit of the indoor unit tochange a wind direction and an air volume of the discharged air, or (ii)the outdoor unit, where the operation mode information includes at leastone result factor that is obtained from at least one machine-learningnetwork. The method also includes performing an operation of switchingto a second operation mode after a time period of operation in the firstoperation mode. The method also includes where an absolute value of afirst temperature change rate during the first operation mode is greaterthan an absolute value of a second temperature change rate during thesecond operation mode. Other embodiments of this aspect includecorresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

Implementations may include one or more of the following features. Themethod further including: based on the air conditioner operating forcooling, maintaining a temperature during a first period from a timepoint when the first operation mode ends, and subsequently increasingthe temperature compared to the temperature of the first period during asecond period; and based on the air conditioner operating for heating,maintaining a temperature during a first period from a time point whenthe first operation mode ends, and subsequently reducing the temperaturecompared to the temperature of the first period during a second period,where the first period and the second period form a time interval of thesecond operation mode. The method where the at least one parameterincludes (i) an indoor initial temperature related to a time at whichthe first operation mode is started, (ii) a target set temperature forthe first operation mode, (iii) a first temperature change rate duringan initial time interval of the first operation mode, (iv) a secondtemperature change rate during the first operation mode, and (v) a timedifference between a start time and an end time of the first operationmode. The method further including: based on a difference between acurrent temperature and a target set temperature being within atemperature band, or based on the difference being maintained for a timeperiod in the second operation mode, performing an operation to switchto the first operation mode. The method further including: performing ahumidity control operation mode based on a humidity level, which issensed at a time of switching from the first operation mode to thesecond operation mode, exceeding a first reference threshold criteria;and releasing the humidity control operation mode based on the humiditylevel reaching a second reference threshold criteria and beingmaintained for a period of time. Implementations of the describedtechniques may include hardware, a method or process, or computersoftware on a computer-accessible medium.

Another general aspect includes a cloud server, including: acommunication unit configured to communicate with a plurality of airconditioners. The cloud server also includes at least one processor; andat least one computer memory operably connectable to the at least oneprocessor and storing instructions that, when executed by the at leastone processor, perform operations including: receiving, from each of theplurality of air conditioners, at least one parameter generated in afirst operation mode of respective air conditioner, where the at leastone parameter is related to at least one temperature individually set inthe respective air conditioner. The operations also include, for each ofthe plurality of air conditioners, using at least one machine-learningnetwork to process the at least one parameter received from therespective air conditioner, and generating, through a learning process,operation mode information for the respective air conditioner. Theoperations also include transmitting, to each of the plurality of airconditioners, the operation mode information for the respective airconditioner, where the operation mode information includes data to set asecond operation mode of the respective air conditioner after a periodof time of operating the first operation mode. An absolute value of afirst temperature change rate during the first operation mode is greaterthan an absolute value of a second temperature change rate during thesecond operation mode of the respective air conditioner. Otherembodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the actions of the methods.

Implementations may include one or more of the following features. Thecloud server where, for each of the plurality of air conditioners, theat least one parameter includes (i) an indoor initial temperaturerelated to a time at which the first operation mode is started for therespective air conditioner, (ii) a target set temperature for the firstoperation mode of the respective air conditioner, (iii) a firsttemperature change rate during an initial time interval of the firstoperation mode of the respective air conditioner, (iv) a secondtemperature change rate during the first operation mode of therespective air conditioner, and (v) a time difference between a starttime and an end time of the first operation mode of the respective airconditioner. The cloud server where the at least one machine-learningnetwork includes: at least one input layer that has the at least oneparameter as at least one input node; at least one output layer that hasthe operation mode information as at least one output node; and at leastone hidden layer arranged between the at least one input layer and theat least one output layer, where weights of at least one node and atleast one edge of the at least one input node and the at least oneoutput node are updated by a learning process of the at least onemachine-learning network.

Implementations of the described techniques may include hardware, amethod or process, or computer software on a computer-accessible medium.

All or part of the features described throughout this disclosure can beimplemented as a computer program product including instructions thatare stored on one or more non-transitory machine-readable storage media,and that are executable on one or more processing devices. All or partof the features described throughout this disclosure can be implementedas an apparatus, method, or electronic system that can include one ormore processing devices and memory to store executable instructions toimplement the stated functions.

The details of one or more implementations of the subject matter of thisdisclosure are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an operation process of an air conditioner operating in arapid mode and a comfortable mode.

FIG. 2 is a front view of a configuration of an indoor unit of an airconditioner according to an implementation of the present disclosure.

FIG. 3 shows a configuration of an internal learning based controlmodule 100 according to an implementation of the present disclosure.

FIG. 4 shows a relationship between a cloud server 300 that performs theexternal learning and an external learning based control module 200 andthe respective components.

FIG. 5 shows a process in which an inner learning based control modulecalculates at least one result factor from at least one learning factoraccording to an implementation of the present disclosure.

FIG. 6 shows a process in which an external learning based controlmodule calculates at least one result factor from at least one learningfactor according to an implementation of the present disclosure.

FIG. 7 shows a process in which an internal learning based controlmodule operates according to an implementation of the presentdisclosure.

FIG. 8 shows a process that an external learning based control moduleoperates according to an implementation of the present disclosure.

FIG. 9 shows that a parameter generating unit calculates an input factoraccording to an implementation of the present disclosure.

FIG. 10 shows a configuration of a learning algorithm included in alearning unit according to an implementation of the present disclosure.

FIG. 11 shows a change in an operation mode when n implementation of thepresent disclosure is applied.

FIG. 12 shows a process in which an operation mode control unit controlsan operation mode according to a load step according to animplementation of the present disclosure.

FIG. 13 shows a configuration of a learning unit according to animplementation of the present disclosure.

FIG. 14 shows an exemplary configuration of a learning unit according toan implementation of the present disclosure.

FIG. 15 shows an interaction of the parameters of an air conditionerthat discharges cooling; air and a provision of a wind direction andcooling air according to an implementation of the present disclosure.

FIG. 16 shows an interaction between the parameters of an airconditioner that discharges heating air and a provision of a wind speedand heating air according to another implementation of the presentdisclosure.

DETAILED DESCRIPTION

Technologies for controlling air conditioners to be suitable fortemperatures preferred by users and for controlling air conditioners foruser convenience have been developed. In the present disclosure, amechanism in which an air conditioner operates in a rapid mode and acomfortable mode will be described in FIG. 1 in more detail.

FIG. 1 shows an operation process of an air conditioner operating in arapid mode and a comfortable mode. When the air conditioner is turned on(S21), the air conditioner is operated in a rapid mode (a rapidoperation period or a rapid period means a period during which a rapidmode is performed) according to a predetermined set temperature Ts(S22). A set temperature Ts may be selected by the user or may beautomatically set according to a predetermined condition. The rapid modeis a configuration that rapidly cools (or heats) a space. Then, in therapid mode, the air conditioner is operated with a maximum coolingcapacity to quickly cool (or heat) an indoor space. In this process,when a set temperature and an indoor temperature are compared to eachother (S23), and a temperature reaches the set temperature, indoorhumidity is confirmed (S24). When the indoor humidity is equal to orgreater than a predetermined numerical value, the air conditioneroperates in a dehumidification mode (S26). On the other hand, when theindoor humidity is equal or less than a predetermined numerical value,the air conditioner operates in a comfortable mode (S25).

Here, the comfortable mode (a comfortable operation period or acomfortable period means a period during which the comfortable mode isperformed) means the operating with a new set temperature Tsa which ishigher than the set temperature Ts. It includes an operation of a powersaving function that maintains comfortable cooling (or heating) bysensing an environment and adjusting a cooling operation output. The airconditioner operates in the rapid mode at first when the air conditioneris turned on. Then, when the temperature of the target space reaches apredetermined temperature, the operation of switching to the comfortablemode is provided.

However, in some scenarios, the comfortable mode and the rapid mode mayhave a limitation in that a change of surrounding environment conditionscannot be dynamically reflected in the operation of the modes. Forexample, parameters that affect an operation of the rapid mode and thecomfortable mode and their values may vary depending on changes inexternal temperature, the number of residing people, or humidity, andthe like. As such, there may be limitations that these parameters arenot fully considered in performing the rapid mode and the comfortablemode.

Generally, an air conditioner includes an indoor unit installed in anindoor environment, and an outdoor unit including a compressor, and aheat exchanger, and the like, to supply the refrigerant to the indoorunit.

In some scenarios, the indoor unit and the outdoor unit of an airconditioner can be controlled separately. Further, at least one indoorunit may be connected to the outdoor unit, and the air conditioner isoperated in a cooling or heating mode by supplying a refrigerant to theat least one indoor unit depending on a requested operation state.

In some systems, an indoor unit of air conditioner is fixedly arrangedin a specific space and operated, and techniques for improving aperformance of the air conditioner may be implemented by collectingoperation information provided from a plurality of indoor units.According to some implementations of the present disclosure, techniquesfor controlling each of the indoor units to operate optimally in variousoperation modes by using operation information provided from theplurality of indoor units, and an air conditioner applying suchtechniques will be described.

Implementations disclosed herein can help address the above-mentionedproblems. According to some implementations, a learning-based apparatusand technique are provided for efficiently operating in a period dividedinto two or more operation modes of an air conditioner based on theparameters generated in an operating period of an air conditioner.

Implementations described herein enable determining an appropriate(e.g., an optimum) operation mode of the air conditioner by having theparameter generated by an indoor unit of the plurality of airconditioners as a learning factor.

Implementations described herein enable controlling an operation of asubsequent step based on the parameter generated in a process of rapidlychanging a temperature during the operation of the indoor unit.

In the present disclosure, the components that form an air conditionermay be classified into an outdoor unit and an indoor unit. An airconditioning; system may be made of one or more outdoor units and one ormore indoor units. The relationship between the outdoor unit and theindoor unit may be 1:1, 1:N, or M:1.

In some scenarios, when the implementations of the present disclosureare applied, the air conditioner can have the one or more parametersgenerated in an operation process as learning factors and determine theoperation mode with respect to the learning factor.

In some scenarios, when the implementations of the present disclosureare applied, the cloud server can determine the operation mode suitablefor each air conditioner after performing a learning process based onthe parameters generated during the operation of the plurality of airconditioners.

In some scenarios, when an implementation of the present disclosure isapplied, after the air conditioner is operated to reach a predeterminedtarget temperature, it is possible to maintain the target temperaturewithin a predetermined range based on a small amount of electrical powerconsumption per unit time.

In some scenarios, when an implementation of the present disclosure isapplied, it is possible to provide a method of estimating alearning-based operation load for an efficient control of cooling orheating of an air conditioner and an apparatus applying such method.

The effects of the present disclosure are not limited to the effectsdescribed above, and those skilled in the art of the present disclosurecan easily understand various effects of the present disclosure based onthe disclosure of the present disclosure.

The present disclosure can be applied to all apparatuses that controlcooling or heating. However, for convenience of explanation, the coolingwill be mainly described. When the present disclosure is applied to theheating, it is possible to apply the implementations of the presentdisclosure to a process of raising a temperature and a mechanism thatmaintains a raised temperature.

FIG. 2 is a front view of a configuration of an indoor unit of an airconditioner according to an implementation of the present disclosure.

An indoor unit of an air conditioner may be a ceiling-installed embeddedtype or a stand type. Alternatively, the indoor unit of the airconditioner may be a wall-mounted type installed on a wall or may be amovable form. FIG. 2 shows a stand type indoor unit 1 in variousimplementations, but the present disclosure is not limited thereto. Theindoor unit 1 may be connected to an outdoor unit 2 arranged in a spaceseparate from another space where the indoor unit 1 is arranged.

The air conditioner may be the stand type that stands on the floor inthe indoor and is installed, which is a subject of an air conditioning.In this case, the air conditioner may further include a base 20 that isplaced on the floor in the indoor to support an air conditioning module10.

The air conditioning module 10 may be installed in a form placed on thebase 20. In this case, the air conditioning module 10 can suck air atthe predetermined height of the indoor to do the air-conditioning.

The air conditioning module 10 may be detachably coupled to the base 20or may be integrally formed with the base 20.

The air conditioning module 10 may discharge air from a blowing unit 15.The blowing unit 15 may be divided for blowing left and right directionas 11 and 12. The air conditioning module 10 can concentricallydischarge air to a front surface. Further, according to theimplementation, the air conditioning module 10 can discharge the airfrom a blowing unit arranged in various directions such as a sidesurface or an upper surface, and the like. The blowing unit 15 cancontrol a wind speed by the operation mode control units 190 and 290,which will be described later. In one implementation, the blowing unit15 can discharge wind of the wind speed including a plurality of steps.For this purpose, one or more individual blowing fans can be controlled.

On the other hand, suction units that suck indoor air can be arranged inthe air conditioning module 10. Further, it is not identified from theoutside, but the control module 100 that controls the indoor unit 1 canbe arranged in the indoor unit 1. For convenience of explanation, theindoor unit 1 is arranged inside of the inside unit 1 with a dashed linein FIG. 2.

The outdoor unit 2 contributes to control a temperature of air (wind)discharged by the blowing unit 15. In one implementation, a compressorof the outdoor unit 2 may compress and discharge a gaseous refrigerantinto a high-temperature and high-pressure state to provide cooling airto the indoor unit 1. In addition, when the present disclosure isapplied to the heating, the outdoor unit 2 can provide heating air tothe indoor unit 1 by using a predetermined heat pump. The manner inwhich the outdoor unit 2 provides the cooling or heating air usingcompressed refrigerant or heat pump to the indoor unit 1 may bevariously provided and the present disclosure is not limited thereto.

The indoor unit 1 exemplarily shown in FIG. 2 measures a state of theindoor air and operates to reach a set state. However, in order for anoperation of the indoor unit to effectively proceed during a process ofreaching a specific state, various elements should be considered beforeor after reaching that specific state. When the operation of the indoorunit is controlled more precisely by using a learning model based oneach of the elements, an efficient operation is possible.

Hereinafter, in the present disclosure, in the first implementation, thecontrol module 100 may be arranged in the indoor unit 1 to generatevarious parameters and perform learning based on the generated variousparameters. Then, a configuration in which the control module 100arranged in the indoor unit 1 determines an operation mode suitable forthe indoor unit based on a learned result will be described. Theconfiguration is referred to as an ‘internal learning based controlmodule’. In the implementation, in the inner learning base, the airconditioner is operated by inputting the parameter into an embeddedlearning unit 160 and using the operation mode information, which is acalculated result factor, in some implementations.

Further, in the present disclosure, in the second implementation, thecontrol module 100 is arranged in the indoor unit 1 to calculate variousparameters, and provides the calculated result to a cloud server. Thecloud server performs the learning based on the parameter that variousindoor units transmit. Then, a configuration in which the cloud serverdetermines the operation mode suitable for the indoor unit based on thelearned result will be described. The configuration is referred to as an“external learning based control module”. In one implementation, in theexternal learning base, the air conditioner is operated by using theoperation mode information, which is a result factor transmitted fromthe cloud server, after the air conditioner transmits the parameter tothe external cloud server.

Further, in the present disclosure, a first period means a period forwhich an air conditioner operates with a first cooling capability (in acase of heating, a first heating capability), in response to a settemperature (a target temperature set by the user). A second periodmeans a period for which the air conditioner operates with a secondcooling capability (in a case of heating, a first heating capability)different from the first cooling capability after the first period.Accordingly, when the user operates an air conditioner at a specifictime point or instructs a smart care, the air conditioner may operatewith a first cooling capability (for example, the maximum coolingcapability) in response to the set temperature. The configuration may bereferred to as a rapid operation mode (or for convenience ofexplanation, as an abbreviation, a rapid mode). The rapid operation modecan be only operated within a preset time range. After the time range,the air conditioner operates in a comfortable operation mode, andinformation needed for a detailed operation in the comfortable operationmode is operation mode information.

The rapid operation mode can start when the air conditioner starts tooperate and a difference between a room temperature and a target settemperature is greater than a predefined value. Alternatively, when theair conditioner receives an input signal that instructs the driving ofthe rapid operation mode, the air conditioner can operate in response tothe received input signal. For example, when the user does not control atemperature additionally by using a remote controller but presses abutton that instructs the operation artificially intelligently orinstructs the operation body-adaptively, the air conditioner executesthe rapid operation mode in response to the pressed button. The airconditioner switches the operation mode to the comfortable operationmode, which will be described later, without an additional user control.

After the temperature of the air conditioner reaches the set temperatureor the maximum time allocated to the rapid mode has elapsed, theparameter generated in the operation in the rapid mode as describedabove may be inputted as a learning factor, and the air conditioner isoperated with the second cooling capability (for example, coolingoperation output for an overload, a standard load, or a small load)different from the first cooling capability. The operation mode may bereferred to as a comfortable operation mode (alternatively, forconvenience of explanation, as an abbreviation, referred to as acomfortable mode). The above-mentioned second cooling capability(cooling operation output for the overload, the standard load, the smallload) corresponds to a load of a space where the air conditioner isinstalled, and the second cooling capability can be determined based onthe operation mode information.

In one implementation, at the overload mode, air volume or a wind speedis increased. Further, in one implementation, at the small load mode,air volume or a wind speed is decreased. Also, in one implementation, atthe standard load mode, air volume or a wind speed may be maintained.

In the first cooling capability, the air conditioner operates accordingto the maximum cooling (or heating) capability to provide the user witha function of the cooling (or the heating), and then use less energywhen it reaches a certain level (or time). Alternatively, in the firstcooling capability, the air conditioner can operate by using a largeamount of energy to decrease a temperature up to a comfortable level.During the operation according to the second cooling capacity, atemperature of air discharged from the air conditioner can be graduallyincreased. As the heating is the opposite case to the cooling, thetemperature can be gradually decreased during the operation according toa second heating capacity.

When the user selects a specific function, for example, a smart care, orsimply push a power-on button on a controller to operate the airconditioner first, the air conditioner performs the rapid operationduring an initial predetermined period, and then, the air conditionermay be operated in response to the predetermined temperature.

FIG. 3 shows a configuration of an internal learning based controlmodule 100 according to an implementation of the present disclosure.

A parameter generating unit 110 generates a parameter such as atemperature or humidity measured or sensed by an indoor unit 1, or arate of change in the temperature or humidity, and time consumed foreach change, and the like. The sensing unit 120 may sense thetemperature or the humidity for the parameter generated by the parametergenerating unit 110. A sensed value may be provided to the parametergenerating unit 110, and the parameter generating unit 110 mayaccumulate the sensed values in a separate memory, and generate theparameter. Thus, the parameter generating unit 110 may extract alearning factor, that is inputted to a learning unit 160, based on anenvironmental factor and product control information that can beidentified by the control module 100 arranged in the indoor unit.

A target state storage unit 130 may include information on a targettemperature or target humidity, and the like, set in an indoor unit 1.The target state may include one or more temperatures or one or morekinds of humidity. Further, the target state storage unit 130 may alsooptionally include two or more temperatures or two or more kinds ofhumidity. In this case, which value to be set as a target temperature ortarget humidity of the currently operating indoor unit 1 can be selectedand set according to the control of a central control unit 150.

An interface unit 140 allows a user to control a temperature, humidity,air volume, and a wind direction, and the like, of an indoor unit 1, andprovides a button type interface, a remote control type interface, or aninterface for a remote adjustment, and the like. Further, the interfaceunit 140 can receive an interrupt input that changes a wind speed, airvolume, or a temperature of the air discharged from a blowing unit 15.The interrupt input may be stored in a learning unit 160 aspredetermined information.

The learning unit 160 may continuously accumulate values of theparameters (learning factors) generated by a parameter generating unit110. The accumulated values of the parameters may be applied to a deeplearning structure inside of the learning unit 160 to determine anoptimum operation mode operated by an indoor unit 1 based on a change ina temperature or humidity, and the like. The operation mode may includevarious modes. In one implementation, the operation mode may include asmall load mode/a standard load mode/an overload mode.

The information inputted as the learning factor of the learning unit 160may be information generated or stored by and in the parametergenerating unit 110 or a target state storage unit 130, and the like. Inaddition, the information inputted as the learning factor of thelearning unit 160 may be information calculated or converted by acentral control unit 150. The learning unit 160 may estimate a level ofload by using a predetermined learning algorithm.

Alternatively, the learning unit 160 can set the relative degree of loadbased on a state of being operated up to now. It is possible to set anoperation mode as −10%, −20%, and the like.

The central control unit 150 may control each of the components and canfinally calculate an operation method needed for the indoor unit 1 tooperate. The operation method of the indoor unit 1 can be divided intovarious methods. For example, it is possible to determine the operationmode information such as levels of loads (overload/standard load/smallload) by estimating a current load in the indoor space with a fewlevels. The learning unit 160 may determine the operation mode, in whichthe indoor unit 1 should operate at present, based on the change in thetemperature or the humidity and the time, and the like, and accordingly,the central control unit 150 can control a specific workload state ofthe air conditioner.

An operation mode control unit 190 operates based on an operation modedetermined by a central control unit 150 and can be diversifieddepending on the type of a determined mode. As described above, when theoperation mode includes the small load mode/the standard load mode/theoverload mode, the central control unit 150 may select one of theoperation modes based on information given by the above-mentionedparameter generating unit 110 and learning unit 160.

The operation mode control unit 190 may control a blowing unit 15 and anoutdoor unit 2 based on the selected operation mode. For example, it ispossible to perform a control, for example, controlling a wind speeddischargeable from the blowing unit 15, or controlling an amount ofgaseous refrigerant that a compressor in the outer unit 2 compresses anddischarges.

In one implementation, when the operation mode set by the operation modecontrol unit 190 is the overload mode, it is possible to increase atleast one of the wind speed and the air volume of the blowing unit 15.In a case of the small load mode, it is possible to decrease at leastone of the wind speed or the air volume of the blowing unit 15. In acase of the standard load mode, it is possible to maintain the windspeed of the blowing unit 15. Similarly, when the operation mode set bythe operation mode control unit 190 is the overload mode, the outdoorunit 2 can control the compressor to operate at the maximum output.Further, in the case of the small load mode, the compressor can bepowered off (off).

That is, the operation mode control unit 190 can control the blowingunit 15 or the outdoor unit 2 based on the operation mode informationthat sets the comfortable operation mode after the period operating inthe rapid operation mode. Based on the operation mode information, theoperation mode control unit 190 can control the wind direction and theair volume of the blowing unit 15.

As mentioned above, the rapid operation mode may only operate within acertain time range (for example, a preset time range such as 20 minutesor 30 minutes, and the like) to quickly change an indoor temperature.Then, the central control unit 150 can instruct the operation modecontrol unit 190 to switch the operation mode into the comfortableoperation mode by using the operation mode information determined basedon the parameters generated in the rapid operation mode.

The operation mode control unit 190 can control the blowing unit 15 andthe outdoor unit 2 in various ways. In the implementation, it ispossible to implement various operation modes by controlling the windspeed and the on/off time period of the compressor in the outdoor unit,respectively.

A configuration of the air conditioner including the internal learningbased control module 100 will be summarized as follows. The airconditioner includes the blowing unit 15 that discharges the air forcooling or heating, and the parameter generating unit 110 thatcalculates one or more parameters during the first period of operation,the learning unit 160 that receives the parameters as the learningfactor and outputs the operation mode information about an operationmode for a second period after the first period, the operation modecontrol unit 190 that controls the blowing unit 15 or the outdoor unit 2in the second period based on the operation mode information, and thecentral control unit 150 that controls the parameter generation unit110, the learning unit 160, and the operation mode control unit 190. Theelectrical power consumption per unit time during the second period isless than the electrical power consumption per unit time during thefirst period. The air conditioners with indoor unit 1 and the outdoorunit 2 operate in the rapid mode during the first period. The airconditioners with indoor unit 1 and the outdoor unit 2 can operate in acomfortable mode during the second period after the rapid mode, with asmall amount of electrical power consumption by using the learningfactors obtained during the operation of the air conditioner in therapid mode.

In one implementation, at least one of an indoor initial temperature ata start time point of the first period (a period where the airconditioner is operated with a first cooling capacity, for example, therapid mode), a target set temperature of the first period, a rate oftemperature change in a preset initial time interval of the firstperiod, a rate of a temperature change in the first period, and a timedifference between a start time point and an end time point of the firstperiod may be the parameter generated during the first period. Here, itis possible to use the information obtained by the sensing unit 120 orthe target state storage unit 130 to generate such a parameter.

On the other hand, when an interrupt input is received from theinterface unit 140 during the operation of the air conditioner duringthe second period (a period where the air conditioner is operated with asecond cooling capacity, for example, in the comfortable mode), it ispossible to update the learning unit 160 based on the received interruptinput.

In more detail, the central control unit 150 may provide the operationmode information and the interrupt input to the learning unit 160 toupdate the learning unit 160 or the operation mode information. When itis changed due to an update of the learning unit 160 or the operationmode information is updated, the operation mode control unit 190 may beprovided with updated operation mode information again.

One or more of the components described with reference to FIG. 3 may beimplemented by at least one processor that is operably connected to atleast one computer memory storing instructions that perform variousoperations described herein when executed by the at least one processor.

FIG. 4 shows a configuration of an external learning based controlmodule according to an implementation of the present disclosure. FIG. 4shows relations among a cloud server 300 that performs externallearning, an external learning based control module 200 and theirrespective components.

Since a parameter generating unit 210, a sensing unit 220, a targetstate storage unit 230, an interface unit 240, and an operation modecontrol unit 290 of the components of the external learning basedcontrol module 200 may be formed in the same manner as the parametergenerating unit 110, the sensing unit 120, the target state storage unit130, the interface unit 140, and the operation mode control unit 190 asshown in FIG. 3, the description thereof can be replaced with thedescription of the same components in FIG. 3.

A central control unit 250 may control the respective components andfinally transmit parameters (i.e., learning factors) needed fordetermining an operation method required for an indoor unit 1 to operateto a cloud server 300 by controlling a communication unit 280. A servercontrol unit 350 of the cloud server 300 may receive a learning factortransmitted by the control module 200 from a communication unit 380 andinput the received learning factor to a learning unit 360 to determinean operation mode suitable for the control module 200. The informationon the determined operation mode may be transmitted to the controlmodule 200 via the communication unit 380.

As shown in FIGS. 10, 13, 14, the learning unit 360 may include anartificial neural network, and learning unit 360 generate links betweenone or more hidden layers and each input/output factor, and bias or theweight of each link of the network in a learning process, and may storeinformation updated from the outside. In this case, the learning unit360 can store the different versions to the cloud server 300.

The cloud server 300 may receive the learning factor from the pluralityof control modules and determine the operation mode in response to thelearning factor. Further, it is possible to update the learning unit 360by continuously inputting the learning factor provided by the pluralityof control modules, to the learning unit 360. The learning unit 360 canestimate a level of load by using a predetermined learning algorithm.

The cloud server 300 of FIG. 4 is summarized as follows.

The communication unit 380 may receive one or more parameters determinedin an operation mode during a first period by M number of airconditioners and transmit operation mode information to the M number ofair conditioners, respectively. The learning unit 360 may receive aparameter of a first air conditioner out of the M number of airconditioners as a learning factor and may output operation modeinformation on an operation mode for the first air conditioner during asecond period after the first period.

A first cooling capacity for the first period and a second coolingcapacity for the second period may be set differently. For example, thefirst cooling capability may be an operation mode in which a wind speed,air blowing volume, and a temperature of a refrigerant, and the like isincreased, so as to rapidly drop a temperature of an target inner spacewith the maximum cooling capability. The above-mentioned operation modeprovides an environment having a certain level of temperatures, where auser can rapidly feel comfortable for certain time (a short period oftime). On the other hand, the second cooling capability may be anoperation mode in which less energy is consumed while maintaining thecertain level of temperatures, after the environment enabling the userto feel a comfort is provided. Alternatively, the second coolingcapability that increases the wind speed or the air volume can also beprovided when the temperature of air discharged from the air conditionercannot reach the level of temperatures giving the user the feeling ofcomfort.

The server control unit 350 can control the learning unit 360 and thecommunication unit 380. Further, the operation mode information may beoutputted such that an electric power consumption per unit time duringthe second period of the first air conditioner is less than a electricalpower consumption per unit time during the first period of the first airconditioner out of the plurality of air conditioners. The outputtedoperation mode information may be transmitted to the air conditioner(the first air conditioner) through the communication unit 380.

In one implementation, the parameters transmitted by each airconditioner may include various kinds of information calculated by eachair conditioner. In one implementation, the parameter generated in thefirst period may be at least one of an indoor initial temperature at astart time point of the first period, a target set temperature of thefirst period, a temperature change rate for a preset initial timeinterval of the first period, a temperature change rate during the firstperiod, and a time different between a start time point and an end timepoint of the first period. Here, it is possible to use the informationobtained by the sensing unit 120 or the target state storage unit 130 soas to determine each parameter.

When the implementations shown in FIG. 3 or FIG. 4 are applied, it ispossible to estimate a load in a current state of the indoor space witha few levels (for example, a small load, a standard load, and anoverload) by learning an environmental factor and control informationuntil a time point when a target temperature is reached after an airconditioner is turned on.

It is also possible to learn a correlation between a learning factoruntil a time point when the target temperature is reached, and atemperature pattern for cooling (or heating) after the targettemperature is reached, and automatically operate a custom cooling mode(or a custom heating mode) according to a determined level of load sothat an automatic operation is possible in a power saving mode, acomfortable cooling (or heating) mode depending on an indoor load levelafter the target temperature is reached without a user additionallyoperating the air conditioner.

Further, when each air conditioner transmits an interrupt inputgenerated during an operation of each air conditioner in the secondperiod, the communication unit 380 can receive the interrupt input fromeach of the air conditioners and update the learning unit 360 based onthe received interrupt input.

In more detail, the server control unit 350 may provide the learningunit 360 with the operation mode information and the interrupt input toupdate the learning unit 360 or update the operation mode information.When the learning unit 360 or the operation mode information is updated,the updated operation mode information may be provided to the airconditioner via the communication unit 380 again.

When the implementations shown in FIG. 3 or FIG. 4 are applied, thelevel of load of the air conditioner can be estimated so as to reflectan environmental element that influences on a cooling efficiency, suchas space size, an insulation state, a difference in temperature betweenan indoor and an outdoor, and the like after the target temperature isreached. Thus, the air conditioner can perform efficient cooling afterthe target temperature is reached, which is a result obtained by thelearning units 160 and 360, that various learning factors are inputtedto the learning units 160 and 360. The learning factors are determinedin a process in which the temperature reaches the target temperature.Learning factors determined in the process in which the temperaturereaches the target temperature are described in the below.

One or more of the components described with reference to FIG. 4 may beimplemented by at least one processor that is operably connected to atleast one computer memory storing instructions that perform variousoperations described herein when executed by the at least one processor.

FIG. 5 shows a process in which an inner learning based control modulecalculates at least one result factor from at least one learning factoraccording to an exemplary implementation of the present disclosure.

A parameter generating unit 110 may generate one or more parameters,that is, one or more learning factors. The internal learning basedcontrol module 100 can obtain at least one result factor using the oneor more parameters. In some implementations, the at least one resultfactor instructs or includes information regarding an operation for anindoor unit to operate, e.g., an operation mode. In an implementation,the one or more parameters that the parameter generating unit 110generates may include an indoor initial temperature sensed at the timewhen an air conditioner starts to operate, a target set temperature, aninitial N minute temperature change rate, a temperature change rate inthe rapid period, a time to reach a target temperature, and the like.The temperature change rate for initial N minutes may be a rate at whicha temperature changes for 3 minutes or 5 minutes immediately after theair condition starts to operate, for example. Of course, a temperaturedifference for a certain time period may be used.

In the implementation, the above-mentioned parameters include variouskinds of information obtainable while the indoor unit 1 performs aninitial operation.

The parameters that the parameter generating unit 110 generates may beinputted in the control module 100 and a learning unit 160 of thecontrol module 100 may perform predetermined learning by using theinputted parameters (learning factors). The learning unit 160 may applya deep learning module. The learning unit 160 may input the inputtedparameter to network that forms deep learning and calculate at least oneresult factor, e.g., which instructs or includes information regardingan operation mode. In one implementation, the parameters are an initialtemperature (TempInit), a target set temperature (TempTarget), atemperature change rate for initial 3 minutes (InitRate), a temperaturechange rate in a rapid period (PowerRate), and a time to reach thetarget temperature (PowerTime). According to the calculated operationmode, the air conditioner is instructed to increase or decrease ancooling (heating) output of an air conditioner to cope with differentload levels, such as an overload/a standard load/a small load, based ona current operation mode. Alternatively, the calculated or generatedoperation mode can numerically adjust an operation output based on theoperation mode currently being performed, or based on the currentoperation condition.

An operation mode control unit 190 may control an indoor unit 1 or anoutdoor unit 2 according to the determined operation mode.

FIG. 6 shows a process in which an external learning based controlmodule calculates at least one result factor from one or more learningfactors according to an implementation of the present disclosure. FIG. 6shows a process in which a plurality of indoor units 1 and the controlmodules and a learning unit 360 of a cloud server 300 calculateinformation on an operation mode required for each indoor unit 1 tooperate.

A parameter generating unit 210 a included in a control module of afirst indoor unit 1 a may generate one or more parameters, i.e.,learning factors, so as to calculate a result that instructs anoperation method (i.e., an operation mode) required for an indoor unitto operate. As shown in FIG. 5, in one implementation, the parameterthat the parameter generating unit 210 a generates may be an initialroom temperature sensed at a time point when an air conditioner startsto operate, a target set temperature, an initial N minutes temperaturechange rate, a temperature change rate in the rapid period, and a timeto reach the target temperature, and the like. The temperature changerate for initial N minutes means a rate at which the temperature changesfor 3 or 5 minutes immediately after the air condition starts tooperate, for example. Of course, a temperature difference for a certaintime period may also be used.

The generated parameter may be transmitted to a cloud server 300 asexemplified in S31 a, and the cloud server 300 may input the receivedparameter to a learning unit 360.

Similarly, a parameter generating unit 210 b included in a controlmodule of a second indoor unit 1 b may generate one or more parameters,i.e., learning factors, so as to calculate a result that represents anoperation method an operation mode) required for an indoor unit tooperate. The generated parameter may be transmitted to a cloud server300 as exemplified in S31 b, and the cloud server 300 may input thereceived parameter to a learning unit 360.

The learning unit 360 in the cloud server may performs a predeterminedlearning by using the inputted learning factors. The learning unit 360may apply a deep learning module. Then, the learning unit 360 may inputthe inputted parameters to the network that forms deep learning tocalculate at least one result factor, e.g., which instructs or includesinformation regarding an operation mode. The operation modes can becalculated or generated for each of the indoor units 1 a and 1 b. Thegenerated operation mode increases or decreases an cooling (heating)output of an air conditioner to cope with different load levels, such asan overload/a standard load/a small load, based on a current operationmode. Alternatively, the determined operation mode can numericallyadjust an operation output based on the operation mode currently beingperformed.

The operating mode determined for each of the indoor units 1 a and 1 bmay be transmitted to each of the indoor units 1 a and 1 b again (S32 aand S32 b) and the operation mode control units 290 a and 290 b of eachof the indoor units may be operated according to the operation mode thatthe cloud server 300 transmits. That is, the operation mode controlunits 290 a and 290 b may control the indoor units 1 a and 1 b and/or anoutdoor unit 2 according to the determined operation mode.

As shown in FIGS. 5 and 6, an environmental factor for a load estimationand product control information are inputted to the learning unit 160,as learning input factor, arranged in the indoor unit 1 or to thelearning unit 360 arranged in the cloud server 300 in order to obtainlearning results. These results may be used to determine an operationmode. In one implementation, the determined operation mode may berepresented as modes for different steps of load, such as an overloadmode/a standard load model/a small load mode). Alternatively, theoperation load (i.e., the cooling operation output of the airconditioner) may be adjusted based on the operation mode currently beingperformed or based on the current operation condition. Such adjustmentmay be expressed in percentage (%).

As shown in FIG. 5, in the method of estimating a load requested fordetermining an operation mode based on the inner learning based controlmodule, the environmental factor and the product control information maybe generated as the parameter referred to as the learning factor and thegenerated parameter may be provided as the learning factor of thelearning unit 160 to calculate a load result by applying a learningalgorithm arranged in the learning unit 160.

As shown in FIG. 6, in the method of estimating a load requested fordetermining an operation mode based on the external learning basedcontrol module, the environment factor and the product controlinformation may be generated as the parameter referred to as thelearning factor and the generated parameter may be provided as thelearning factor of the learning unit 360 of the cloud server 300 tocalculate a load result by applying a learning algorithm in the learningunit 360.

In the implementation as shown in FIG. 6, after each air conditionerstarts to do a smart care, and operates according to the maximum coolingcapacity, and then after it enters a comfortable mode or a certain timehas passed, it is possible to transmit the predetermined parameter tothe cloud server 300 and receive operation mode information that sets acurrent cooling operation output of air conditioner so as for the airconditioner to perform the comfortable mode. The smart care operationmeans that the air conditioner operates the rapid mode and thecomfortable mode automatically without user's control.

FIG. 7 shows a process in which an internal learning based controlmodule operates according to an implementation of the presentdisclosure.

In an air conditioner driven on a learning base, a blowing unit 15 maydischarge air for cooling or heating during a first period (S40). In oneimplementation, during the first period, the air conditioner may operatein a rapid mode, and during a second period after the first period theair conditioner may operate in a comfortable mode. During the firstperiod, a target temperature may be reached in a short period of time.During the second period, it is possible to maintain an indoortemperature within a predetermined deviation range from the targettemperature.

A parameter generating unit 110 calculates an input factor to beinputted to a learning unit 160, i.e., a learning factor (S41). That is,the parameter generating unit 110 can generate various parameters in thefirst period. When the input factor is provided to the learning unit 160(S42), the learning unit 160 estimates a load (S43). In oneimplementation, the learning unit 160 receives the generated parameteras a learning factor to output operation mode information for the secondperiod after the first period.

Thereafter, when a central control unit 150 provides an estimated loadstep information (outputted operation mode information) to an operationmode control unit 190 (S44), the operation mode control unit 190controls an operation mode of an indoor unit 1 and/or an outdoor unit 2(S45).

In one implementation, as exemplified in S44 and S45, when the centralcontrol unit 150 provides the operation mode control unit 190 with theoperation mode information outputted by the learning unit 160, theoperation mode control unit 190 controls the blowing unit 15 or theoutdoor unit 2 in the second period based on the operation modeinformation. Based on the process in FIG. 7, in comparison of anelectrical power consumption per unit time during the first period ofthe air conditioner with an electrical power consumption per unit timefor the second period, the latter is less than the former. That is,after operating in the rapid mode so that an indoor temperature reachesa target temperature within a short period of time in the first period,the temperature of reaching the target temperature may be maintainedbased on the small amount of electrical power consumption at the targettemperature or an indoor temperature may be maintained within apredetermined range. The operation mode information on the second periodmay be determined depending on the parameters generated by the learningunit 160 in the first period. Therefore, when the parameters generatedin the first period are different from each other, the operation modeinformation on the second period may vary.

FIG. 8 shows a process in which an external learning based controlmodule operates according to an implementation of the presentdisclosure.

A parameter generating unit 210 may extract an input factor to beinputted to a learning unit 360 of a cloud server 300, i.e., a learningfactor (S51). When the input factor is transmitted to the cloud server300 (S52), the learning unit 360 of the cloud server 300 may estimate aload (S53). At this time, as the cloud server 300 receives the learningfactor from the plurality of products, it is possible to estimate a loadfor each air conditioner by inputting the learning factor to thelearning unit 360 for each air conditioner. Then, the cloud server 300transmits an estimated load step to the air conditioner, i.e., operationmode information (S54). A central control unit 250 of each airconditioner provides the load step received from an operation modecontrol unit 290 and the operation mode control unit 290 of each airconditioner controls an operation mode of an indoor unit 1 and/or anoutdoor unit (S55).

The process of FIG. 8 is described below.

The cloud server controls a driving of a plurality of air conditionersbased on learning. In each of the air conditioners, the parametergenerating unit 210 generates a parameter in an operation mode of afirst period (S51). A communication unit 380 receives one or moreparameters generated in the operation mode of the first period from afirst air conditioner of the plurality of air conditioners (S52). Theprocess may occur in a continuously accumulative manner and theparameters generated by the plurality of air conditioners may beaccumulated in a cloud server 300, and the cloud server 300 may have adatabase additionally.

The learning unit 360 receives the parameter received from each of theair conditioners as the learning factor and outputs operation modeinformation on an operation mode of the air conditioner for the secondperiod after the first period (S53). Through a control of a servercontrol unit 350, the communication unit 380 can transmit the operationmode information outputted by the learning unit 360 to each of the airconditioners.

FIG. 8 shows that the cloud server 300 provides operation modeinformation needed for the air conditioner to operate during the secondperiod based on the parameters transmitted by each air conditioner. Atthis time, an electrical power consumption unit per unit time during thesecond period of the air conditioner is less than an electrical powerconsumption per unit time during the first period of the airconditioner.

In FIG. 8, the learning unit of the cloud server may obtain variouskinds of information needed for estimating a load in a learning processor may directly receive the information from an outside. For example,the learning unit of the cloud server may include a hidden layer asshown in FIGS. 10 and 13, and the like. Information on a setting foreach node of the hidden layers, a link or bias between the two nodes,the weight of a link or a node may be preset and then changed in thelearning process. Further, it is possible to apply the informationobtained from additional monitoring and learning to the learning unit.Here, the outside means a separate server from the cloud server.Alternatively, it is possible to couple a storage medium such as adistinct memory card to the cloud server to apply the information storedin the storage medium to the learning unit.

Based on the implementation in FIG. 7 or 8, it is possible to apply amethod of estimating a learning-based load for an efficient coolingcontrol of an air conditioner. In this process, a load estimation (anoutput of operation mode information) for the second period may be madefor each step through learning based on the environmental factors andcontrol information which are obtainable at time point when a targettemperature is reached, and/or obtainable from a temperature changepattern up to a certain time point after the target temperature isreached.

Further, according to an estimated load level, the temperature is set sothat a power saving or comfortable cooling is possible during the secondperiod, and the learning units 160 and 360 may output operation modeinformation according to which air volume/wind direction, and the like,can vary.

As shown in FIGS. 3 and 7, in one implementation, when a learning logic(learning unit) 160 for load estimation is provided on the product, theinternal learning based control module 100 may be installed in the airconditioner. Further, when the learning logic (learning unit) 360 forload estimation is provided on the cloud server, the external learningbased control module 200 is installed in the air conditioner, and thecloud server 300 receives the environmental factor and the productcontrol information from the control module 200 through the wirelesscommunication, and analyzes or re-learns it.

FIG. 9 shows that a parameter generating unit obtains an input factoraccording to an implementation of the present disclosure. In the graph,the temperature is expressed as a Y-axis and the time is expressed as anX-axis. The graph shows a change in temperature with time. FIG. 9 showsan example of an input factor for learning, and an example of Obtaininga learning factor such as control information on a product end and atemperature change rate obtained from a temperature change with time.

In the above implementation, as an input factor generated by a parametergenerating unit, an indoor initial temperature, a target settemperature, and a temperate change rate for initial N minutes, atemperature change rate in a rapid period, and a time to reach thetarget temperature, and the like are described. Here, N can be selectedin various ways. In some cases, N is 3 minutes in the implementation inFIG. 9.

FIG. 9 shows that the parameter generating units 110 and 210 cangenerate an initial temperature (TempInit) and a target set temperature(TempTarget). The initial temperature (TempInit) can be generated bysensing the indoor initial temperature by the sensing units 120 and 220.The target set temperature (TempTarget) may be generated based on atarget set temperature stored in the target state storage units 130 and230. On the other hand, the parameter generating units 120 and 220 cancalculate a temperature change rate for initial three minutes (InitRate)as a/b.

The term “b” means a time passage after an air conditioner operates. Forexample, “b” may be three minutes or five minutes. The term “a” means amagnitude of a temperature change in TempInit during time b.

On the other hand, the temperature change rate in the rapid period(PowerRate) can also be calculated as c/d. The term “c” means atemperature difference between TempTarget and TempInit. Therefore, theterm “c” may be “TempInit−TempTarget”. The time (PowerTime) at which thetemperature reaches a target temperature can be calculated as “d”. Inone implementation, d means time consumed to reach a set temperature asquickly as possible by performing an operation with the maximum coolingcapacity for the maximum of M minutes, and includes a time magnitude of15 minutes or 20 minutes in one implementation. It is possible todetermine an overload/a standard load/a small load by setting a coolingcapability to be performed in a comfortable mode (a second period) attime point “d”.

The mode in a first period (a rapid operation mode or a rapid period)enables the air conditioner to drive and the temperature of the airconditioner to reach a target temperature. In an implementation, in thefirst period mode, the air conditioner is operated in a high speedcooling manner till the target set temperate with a maximum coolingpower of the air conditioner at the beginning of cooling.

In FIG. 9, the target temperature may be set to a specific targettemperature value, but may be within a certain range. For example, whenthe target temperature value is 20 degrees, the current temperature mayreach 20 degrees as an example of reaching the target temperature.However, in another implementation of reaching the target temperature,it is possible to determine to reach the target temperature even when acurrent temperature reaches a state of +1° C. or −1° C. (i.e. 19° C. to21° C.) based on 20 degrees.

The determination of reaching the target temperature can be applied to acase where a range of time at which the air conditioner can be operatedin the rapid period is preset. For example, it is assumed that the timeat which the air conditioner is operated in the maximum cooling capacitywith rapid period (rapid enable time) is predetermined such as 10minutes or 15 minutes. If the air conditioner operates and does notreach the target temperature even if the air conditioner is operatedeven after the rapid enable time, the parameter generating units 110 and210 may take a current temperature as a learning factor instead of thetarget, temperature.

Then, when the temperature of the air conditioner reaches the targettemperature, it is possible to change the mode for the first period (therapid period) with another mode for the second period (a comfortableperiod). Further, even if the temperature of the air conditioner doesnot reach the target temperature, when a certain time has passed or acurrent temperature closely approaches the target temperature, theoperation mode of the air conditioner can be changed to the comfortablemode. In one implementation, the second period (the comfortable period)is a comfortable operation, and includes maintaining the set temperatureafter reaching the target temperature and operating in an auto mode(indirect wind).

When an external influence to the space is significant or a space iswide, An indoor temperature cannot reach a target temperature.Accordingly, it is possible to change the mode for the rapid period (thefirst period) with another mode for the comfortable period (the secondperiod) even when the temperature of the air conditioner reaches thetarget temperature in some degrees.

Then, the operation mode for the second period (the comfortable period)may be selected in a different manner with the first period (the rapidperiod). As described above, the learning units 160 and 360 may use fivelearning factors (TempInit, TempTarget, InitRate, PowerRate, andPowerTime) that the parameter generating units 110 and 210 generate anoperation mode of an air conditioner in a comfortable period, i.e., anoperation mode.

In FIG. 9, an absolute value of a rate of temperature change per hour inthe first period operating in the rapid operation mode is greater thanan absolute value of a rate of temperature change per hour in the secondperiod operating in the comfortable operation mode. The temperature ischanged at a rapid speed in the rapid operation mode and the changedtemperature is maintained in the comfortable operation mode.

Further, the difference between the temperature of the space and thetarget set temperature is included in a preset predetermined temperatureband or the difference is maintained for certain time in the secondperiod (the comfortable operation mode), the central control units 150and 250 may instruct a switch to a rapid operation mode to the operationmode control units 190 and 290 for rapid cooling or heating the space.In this case, the operation mode control units 190 and 290 can control ablowing unit 15 and an outdoor unit 2 so that the air conditioneroperates in the rapid operation mode for rapidly lowering thetemperature of the space as shown in the first period. When a differencein temperature is 2 degrees or more, or when a certain level ofdifference in temperature is maintained for 5 minutes or more, the airconditioner can be operated in the rapid operation mode to lower theroom temperature quickly.

In addition, it is possible to switch the operation mode to a humiditycontrol operation mode when the humidity is a predetermined reference ormore (for example, 70% or more) in the comfortable operation mode orwhen the humidity is maintained for 5 minutes or more. When the centralcontrol units 150 and 250 may continuously monitor the humidity, and thehumidity reaches the predetermined reference, it is possible to instructthe operation mode control units 190 and 290 so as to stop thecomfortable operation mode and start to operate in humidity controloperation mode. Then, when the humidity reaches a certain level or less(for example, 50% or less) and this humidity is maintained for a certaintime or more (for example, 5 minutes or more), it is possible to releasethe humidity control operation mode and start the comfortable operationmode again.

According to the implementation described in FIG. 9, it is possible toestimate a load based on the environmental factors that the airconditioner can obtain. This may includes obtaining a property of acooling environment in a space, which the air conditioner is arranged,as various learning factors and determining the load based on theobtained various learning factors. Further, determining the load is notbased on a simple function, but it is possible to determine an optimumload with respect to the learning factor by using a deed learningalgorithm that a learning unit 360 of a cloud server 300 or a learningunit 160 in a control module 100 in the air conditioner provides. As aresult, in the second period, the air conditioner can be operated byselecting a power saving or comfortable cooling, and the like, accordingto a load step.

Conventionally, as an environmental change is not considered after therapid period, there is a problem that a user controls the temperatureagain in the comfortable period after the rapid period ends. However, inthe implementation of the present disclosure, it is possible to maintaina cooling state without controlling the temperature additionally for theuser in a comfortable period state based on initial learning andcontinuous learning.

Particularly, in order to provide a comfort to the user withoutcontrolling an air conditioner manually, the air conditioner rapidlyperforms the cooling (or the heating) to the vicinity of the targettemperate at the beginning of the operation of the air conditioner. Whenit reaches the vicinity of the target temperature, the air conditionermay determine whether to maintain the operation mode in the first periodas itself or whether to consume a large amount of electrical powercompared to the operation mode of the first period, or whether toconsume less amount of electrical power, and the like, while maintainingthe cooling or the heating. In one implementation, a load determinationis performed.

The values inputted to the learning units 160 and 260 for the loaddetermination are the parameters of the change in the time or thetemperature, an initial value, a result value, or the magnitude thereofcalculated in the above described first period.

In addition, the central control units 150 and 250 or a server controlunit 350 of the cloud server 300 can detect a state of changing thetemperature during the operation of the air conditioner according to anoperation mode determined in a learning process. In this case, the nodesor the links of the hidden layers that form a deep learning of thelearning units 160 and 360 may be reconfigured or the weights may bechanged so that a more suitable operation mode can be determined.

On the other hand, in FIG. 9, when humidity sensed at an end time pointof the first period is equal to or greater than a set reference, thecentral control units 150 and 250 can instruct the humidity controloperation mode to the control units 190 and 290. For example, when thehumidity is 60% or more, it is possible to add a dehumidificationfunction depending on the operation mode of controlling the humidityinstead of the comfortable operation mode.

When the humidity reaches the set reference or less (for example, 50% orless) and the state is maintained for certain time or more (for example,5 minutes or more), the control unit may release the humidity controloperation mode and enter a comfortable operation mode.

FIG. 10 shows a configuration of a learning algorithm included in alearning unit according to an implementation of the present disclosure.A node-based structure of the input factor, the hidden layer that formsthe learning structure, and the output factor are an example of a designfor a learning structure.

The factors that the parameter generating units 110 and 210 generate maybe inputted to an input layer of the learning units 160 and 260. Fivefactors are provided, but various factors may be applied according tothe implementation.

A plurality of hidden layers are arranged in the learning units 160 and260. The learning units 160 and 260 can calculate a correlation betweenthe inputted factors and an edge of each layer. For example, threehidden layers (hidden layer 1, hidden layer 2, and hidden layer 3) maybe arranged as shown in FIG. 10.

For example, each of the nodes of the input layer has total of fiveinput values. The value inputted to five input nodes can be converted inthe input layer or outputted without conversion.

The values outputted from these input layers may be optionally the inputvalues of 12 nodes in a first hidden layer (hidden layer 1). Similarly,the first hidden layer (hidden layer 1) may apply the weights of thelinks to the inputted value and calculate an output value according tologic of each node.

The values outputted from the first hidden layer may be optionally inputvalues of eight nodes of the second hidden layer (hidden layer 2) again.Similarly, the second hidden layer (hidden layer 2) may apply theweights of the links to the inputted values and calculate the outputvalues according to the logic of each node.

The value outputted from the second hidden layer may be optionally theinput values of four nodes of the third hidden layer (hidden layer 3)again. Similarly, the third hidden layer (hidden layer 3) may apply theweights of the links to the inputted values and calculate the outputvalues according to logic of each node.

Finally, an output node (output) can generate the degree of load asthree nodes. In the case of the above described overload/standardload/small load, each value can be inputted to an output node expressedas over, an output node expressed as medium, and an output nodeexpressed as under, respectively.

In one implementation, when the output node expressed as over is 1 andthe output node expressed as medium is 0, and the output node expressedas under is 0, respectively, it is possible to instruct the operationmode as the overload. Meanwhile, when the output node expressed as overis 1 and the output node expressed as under is 1, and the output nodeexpressed as medium is 0, it is possible to indicate the operation modeas the standard load. In the other implementation, when the output nodeexpressed as over is 0 and the output node expressed as under is 1, andthe output node expressed as medium is 0, it is possible to indicate theoperation mode as the small load. Meanwhile, when the output nodeexpressed as over is 0 and the output node expressed as under is 0, andthe output node expressed as medium is 0, it is possible to indicate theoperation mode as the standard load.

In FIG. 10, the links between the nodes of the respective layers are notshown. The link can be newly created or added in a learning process inwhich the learning units 160 and 260 adjust an algorithm or a componentof the algorithm, and the weight assigned to each link can also bechanged. Further, the number of hidden layers can be increased ordecreased, and the number of nodes included in the hidden layers canalso be increased or decreased.

For the hidden layers and the input factors of FIG. 10, an actual use(field) data in a great deal of households for extracting theinput/output factor and chamber (experimental) data for a standardenvironmental test may be collected initially and the pre-learning ismade based on the collected information, to set an initial weight valueof the hidden layer. Then, through a cloud server, or when a controlmodule 100 in the same indoor unit continuously collects actual usedata, re-learning of entire data may be applied to periodically performa weight update for each node and each link of a hidden layer.

In one implementation, it is possible to utilize clustering technique(unsupervised learning: k-means algorithm) in the classification of aload reference by collecting a DB for certain time when an airconditioner is actuated for determining an output factor.

Further, each hidden layer can use a general deep learning method.However, in another implementation, it is possible to change a structureof each hidden layer for learning or to update the weight. For example,a cloud server 300 may enable the information provided by the pluralityof indoor units to be stored in DB and analyze the information stored inthe DB to change the structure of the layers, the node, and the link ofFIG. 9. Further, it is possible to sense that a user adjusts atemperature within certain time in a comfortable period state aftersetting a target temperature, and reflect the sensed temperature. InFIG. 13 to be described later, adjusting a wind speed, air volume, or atemperature, and the like, for the user may be included in the inputnode of the learning units 160 and 360 as an interrupt input.

FIG. 11 shows a change in an operation mode when an implementation ofthe present disclosure is applied.

As shown in FIG. 9 in the above, when the air conditioner starts tooperate, the parameter generating units 110 and 210 may continuouslysense the temperature to calculate a graph as shown in FIG. 11. In thisprocess, it is possible to obtain the learning factors. FIG. 11 showsthe three different graphs. First, the graph in which an operation modeis calculated as the over-load mode is indicated by an dashes line(indicated by G-Over), and a graph in which the operation mode iscalculated as the standard-load mode is indicated by a solid line(indicated by G-medium), and a graph in which the operation mode iscalculated as the small-load mode is indicated by an alternate long andshort dash line (indicated by G-Under).

In a first period, a compressor sharply reduces a temperature within ashort period of time with a maximum output or high output. In oneimplementation, the first period is a rapid period. A Powertime is atime point at which the temperature of the air conditioner reaches atarget temperature (TempTarget) or the temperature of the airconditioner reaches a target temperature with a constant temperaturedifference depending on a state of space. Until the time point, theparameter generating units 110 and 210 may generate various parametersand provide the generated parameters to the learning units 160 and 360.The learning units 160 and 360 may determine an operation mode by usingthe parameters provided at a time point “Powertime”. The learning units160 and 360 can analyze a change pattern according to cooling in thefirst period at the time point expressed as “61”. An air conditioner ina second period can select an operation mode that reduces electricalpower consumption as well as providing a comfort to the user.

When the temperature of the air conditioner does not sufficiently reachthe target temperature in the first period according to the changepattern or a lot of time is consumed to reach the temperature, or anoverload operation mode is determined by the learning units 160 and 360by reflecting a change rate during the initial N minutes, and the like,the air conditioner can be operated for the second period according to acontrol pattern preset for the overload mode as indicated by G-over inthe graph.

When the temperature of the air conditioner sufficiently reaches thetarget temperature in the first period according to the change patternor the time to reach the temperature is consumed in response to areference value, or a standard load operation mode is determined by thelearning units 160 and 360 so as to maintain a current temperature orthe target temperature by reflecting a change rate for initial Nminutes, the air conditioner can be operated for the second periodaccording to a control pattern preset for the standard load mode asindicated by G-medium in the graph.

When the temperature of the air conditioner reaches the lowertemperature than the target temperature or less time is consumed toreach the temperature in the first period according to the changepattern, or a small load operation mode is determined by the learningunits 160 and 360 by reflecting a change rate for initial N minutes, andthe like, the air conditioner can be operated for the second periodaccording to a control pattern preset for the small load mode asindicated by G-Under in the graph.

Even if the target temperatures are the same, the air conditioner canoperate differently when the temperature change patterns until thetemperature of the air conditioner reaches the target temperature aredifferent. That is, the temperature change patterns after the targettemperature is reached are based on the different results of a learningprocess due to the difference values of the learning factors input tothe learning process, and thereby it is possible to perform operationmodes for appropriately performing cooling more or less in response tothe load conditions of a target indoor space.

Conventionally, if the cooling (or the heating) is performed in thefirst period in the same manner in spite of different indoor loadconditions, a weak cooling (or weak heating) or the super-cooling (orsuper-heating) occurs depending on the load conditions. However, by theimplementations of the present disclosure being applied, it is possibleto cool (or heat) in the second period in a manner appropriate for eachof different indoor load condition, so that it is possible to maintain acomfortable indoor environment as well as reducing energy consumptionfor the second period.

FIG. 12 shows that an operation mode control unit controls an operationmode according to an implementation of the present disclosure. In animplementation, the case where a small load is determined will bedescribed. An air conditioner operates in a rapid mode so that thetemperature of the air conditioner reaches a target temperature from aninitial temperature (TempInit). In FIG. 12, in a time domain indicatedby a first period, the air conditioner can operate in the rapid mode asit is in a state before reaching a target set temperature. In this case,the air conditioner can operate in a cool power mode by using a maximumcooling power of the air conditioner. In this process, the airconditioner may sense the human body or sense a space area to lower atemperature in space more efficiently.

On the other hand, as a time domain indicated by a second period is in astate after reaching a target set temperature, an air conditioner canoperate in a comfortable mode. According to an implementation of thepresent disclosure, a learning unit 160 of a control module or alearning unit 360 of a cloud server may determine an operation mode byusing a learning factor that the parameter generating units 110 and 210generate at a time point t1. That is, in the second period, the loadrequired for the air conditioner to operate is determined. As a resultof a determination, in FIG. 12, the air conditioner is operated bydetermining the operation mode as a small load. In the second periodafter the rapid mode in the first period, the operation mode controlunits 190 and 290 can control an indoor unit and an outdoor unit, andthe like, so as to lower the human body adaptation time and raise atemperature, and lower air volume.

By controlling a small load by the operation mode control units 190 and290, a temperature rises to some extent as Temp-aa at a time point t2from a state of the target temperature Temp-a at the t1 point, and atemperature rises as Temp-ab again at a time point t3 to maintain thetemperature to Temp-ab. In one implementation, the target temperature isautomatically set to the temperature that a user generally uses, and maybe a temperature that the user sets recently at most for N times (forexample, 20 times). Alternatively, a temperature preferred by anexternal server based on big data in response to a current temperaturemay be set as a target temperature.

At time point ‘t1’, it is possible to set a cooling capacity after timepoint t1 based on various parameters generated to reach the targettemperature. For example, it is possible to set whether to provide thecooling capacity corresponding to an overload condition, or to providethe cooling capacity corresponding a standard load condition, or toprovide the cooling capacity corresponding to a small load condition,based on previously learned information and the generated parameter.Thereafter, it may operate in a power saving operation mode by graduallyadjusting the cooling capacity between steps t2 and t3. When humidity ishigh in this process, it is possible to adjust the humidity to targethumidity according to an additional process of controlling the humidity.It is also possible to calculate time points t2 and t3 that raise thetemperature according to a pattern of the user.

Although it is not shown in the drawing, when the operation mode isdetermined as the standard load, it is possible to raise a temperatureand to convert air volume into a weak wind by reflecting the human bodyadaptation time with a standard condition. Further, when the operationmode is determined as the overload, it is possible to enhance an airvolume without raising a temperature depending on the human bodyadaptation condition.

According to one implementation, it is possible to determine an overloadmode/a standard load mode/a small load mode as an operation type of acomfortable operation mode based on the information determined in aprocess of operating in a rapid operation mode. When the operation modeof the comfortable operation mode is the overload mode, the operationmode control units 190 and 290 can control the air conditioner in amanner of reducing or maintaining the air volume by controlling the airvolume as moderate wind or mild wind.

On the other hand, when an operation mode of the comfortable operationmode is the small load mode or the standard load mode, the operationmode control units 190 and 290 can control the air conditioner in such amanner of controlling air volume as the mild wind to reduce the airvolume.

Further, in the operation mode of the comfortable operation mode, theoperation mode control units 190 and 290 send the wind to many areas bysetting a wind direction of a blowing unit 15 upward.

For example, the operation modes before/after reaching the targettemperature may be classified into a first operation mode of operatingwith a maximum capacity of the air conditioner until the temperaturereaches to the target temperature and a second operation mode ofoperating according to the load determined at the time point t1 when thetarget temperature is reached. It is possible to provide variousoperation modes through the temperature and air volume change for powersaving according to a load step estimated at time point ‘t1’ or acomfortable cooling.

A starting temperature in which the air conditioner starts to operateand a target temperature, and the variables in various environments thatoccur between the starting temperature and the target temperature areextracted as a learning factor. The air conditioner may change theoperation of the air conditioner to the comfortable mode after the rapidmode that uses the maximum cooling power. In this process, the user cancontrol the operation of the air conditioner by selecting an appropriateload so as not to feel the temperature warming up for the user.

Further, when an external operation (such as increasing or decreasing atemperature or controlling air volume) occurs that controls theoperation of the air conditioner in the process of determining the loadusing various environmental factors and operating in a comfortable mode,it is possible to more accurately estimate the load by inputting theexternal operation to the learning units 160 and 360 so as to be newresult node.

It will be summarized below. In one implementation, the cooling airconditioner maintains a temperature at the time point when the rapidoperation mode is terminated in period t1-t2 based on the end time pointt1 of the rapid operation mode. Then, in a period t2-t3 after the periodt1-t2, a temperature is increased compared to the period t1-t2 with astepwise change rate. In one implementation, in a heating airconditioner, after maintaining the temperature in the period t1-t2 basedon the end point t1 of the rapid operation mode, in the period t2-t3after the period t1-t2, it may reduce the temperature compared to theperiod t1-t2 with the stepwise change rate. Here, in one implementation,the stepwise change rate means that a temperature changes slowly or atemperature raises in a stepwise as shown in FIG. 12. The airconditioner can operate so that a temperature changes in a straight lineor a broken line or a curved line at a Temp-a point at time point t1,Temp-aa point at time point t2, and Temp-ab point at time point t3.

The time intervals of t1-t2 and t2-t3 can also be increased ordecreased. For example, the interface units 140 and 240 may receive acontrol signal that increases or decreases a temperature in the periodt1-t2 during the operation of the air conditioner. For example, the userchanges the temperature in the period t1-t2. The central control units150 and 250 may increase a temporal length of the period t1-t2 when acontrol signal that lowers a temperature is received. For example, thetime can be increased by 1 minute. On the contrary, the central controlunits 150 and 250 can reduce the temporal length of the period t1-t2when the control signal that increases the temperature is received. Forexample, the time can be reduced by 1 minute. This is also identicallyapplicable to the case where a control signal is received in the periodt2-t3. As a result, it is possible to increase or decrease the intervalbetween the two time points at which the control signal is generated,such as t1-t2 or t2-t3 in an inverse proportional to the increase or thedecrease of the temperature, in response to the control signal thatincreases or decreases the temperature in the comfortable operationmode.

Further, according to the implementation of the present disclosure, thecontrol units 150 and 250 can store position information on a residingarea that an occupant is repeatedly confirmed in a space where the airconditioner is installed. That is, information on an area where a personis mainly sensed or a person is frequently in the entire space wherewind of the air conditioner is transmitted may be stored as positionalinformation on the residing area, and the residing area may beconcentrated as a subject in the rapid operation mode, so that the airconditioner can operate. For this purpose, the left and right winddirection of the blowing unit 15 in the rapid operation mode can be setbased on the residing area. Then, in the comfortable mode, based on thepositional information that the control units 150 and 250 store, forexample, positional information on a activity area that the occupant isintermittently identified including the residing area, the left andright wind direction of the blowing unit 15 can be set based on theactivity area in the comfortable operation mode. In the rapid operationmode, a specific area (the residing area) stored in the control units150 and 250 may be blown in concentration thereon, and in thecomfortable operation mode, it may blow in concentration on a wider area(a activity area) including the residing area to improve energyefficiency.

In order for the control units 150 and 250 to distinguish the residingarea from the activity area, the air conditioner may use a camera thatacquires an external image, and recognize an area where an occupant isdisposed in an indoor space including a plurality of areas from theimage that the camera acquires, and include artificial neural networkthat pre-learns the result of recognizing a position of the occupant bymachine learning and divide the areas into the residing area that blowsin concentration on a plurality of areas with an input of data to theartificial neural network and the activity area including the residingarea. With respect to the information, positional information(left/right and distance information on an air conditioner) may beprovided from an external server.

As shown in FIG. 12, operation mode information that the learning unitdetermines with respect to the second period may include timeinformation (t2 and t3) and temperature information (Temp-ab andTemp-aa).

FIG. 13 shows a configuration of a learning unit according to animplementation of the present disclosure. The configuration of thelearning units 160 and 360 of FIG. 3 or FIG. 4 in the above will bedescribed.

The learning units 160 and 360 may include an input layer (input) havingN number of parameters as the input nodes, an output layer (output)having operation mode information as an output node, and one or more ofM number of hidden layers arranged between the input layer and theoutput layer. In an implementation, the parameter may be the factorsdescribed in FIG. 5 or FIG. 6 in the above, but is not limited thereto.

Here, the weight may be set at an edge connecting the two nodes of thelayers, and the presence or absence of the weight or the edge can beadded or removed or updated during learning. Thus, the weights of thenodes and the edges arranged between k number of input nodes and inumber of output nodes can be updated in the learning process or by aninterrupt input. As shown in FIG. 13, i number of output nodes may bearranged so as to output the value such as 1/0 or probability for eachmode. Alternatively, as the output node, a node that outputs an element(+, −, or +10% or −20%) that has to be relatively changed in anoperation mode in a first period may be arranged. Alternatively, t2, t3or Temp-aa, Temp-ab, and the like, described in FIG. 12 may also formthe output node.

All nodes and edges may be set to an initial value before the learningunits 160 and 360 perform the learning. However, when the information isaccumulatively inputted, the weights of the nodes and edges in FIG. 13may be changed, and a matching of the parameters generated in the firstperiod and operation mode information suitable for a second period canbe made. In particular, when a cloud server 300 is used, as the learningunit 360 can receive many parameters, the learning unit 360 can performlearning based on a large amount of data.

An interrupt input means information on the changed wind speed ortemperature by a user, after operation mode information on a secondperiod is outputted. Thus, after k number of parameters are inputted inthe first period and the operation mode information on the second periodis generated, and then, when the interrupt input is received again, apredetermined value may be inputted to an distinct node (Interrupt P) togenerated new operation mode information or update the learning units160 and 360.

In summary, the weights of the nodes and the edges between the inputnode and the output node included in the learning units 160 and 360 ofFIG. 13 can be updated in the learning process of the learning units 160and 360 or the interrupt input of a central control unit.

FIG. 14 shows an exemplary configuration of a learning unit according toan implementation of the present disclosure. The learning units 160 and360 include five input units, three hidden layers, and three outputunits. A first hidden layer includes 20 units, and a second hidden layerincludes 13 units, and a third hidden layer includes 5 units. A link mayalways be arranged between the two nodes and it is possible to set theweight of the link.

The inputted values may be an indoor initial temperature, a target settemperature, initial N minutes (for example, 3 minutes), a temperaturedifference, a temperature difference in the rapid period, and a time toreach the target temperature. These five input values may be combined tomap as 20 units. The bias and the weight of the link needed for themapping may be continuously learned at an initially set value, and thevalues may be changed during learning.

Similarly, 20 units are mapped as 13 units of the second hidden layerand 13 units of the second hidden layer are mapped as 5 units of thethird layer again. Five final units are connected to 3 output units, andeach output unit represents a load degree. That is, each output unit maybe determined as any one, which can be connected to a standard load, anoverload, and a small load. Of course, according to the implementationmethod, the output unit may be one, and it is possible to determine theload degree by having an outputted value as 0, 1, and 2.

The link between the two layers or the weight and the bias applied tothe links can be continuously changed during learning. Alternatively,the information on these learning units 160 and 360 may be updatedexternally to set the weight and the bias.

In one implementation, in comparison of an input unit with a unit of thefirst hidden layer, as the weight value of the node of 20 hidden layersis set for each inputted node, 100 weights may be generated for 5 inputnodes in total. Further, since a bias value which is added for additionafter the multiplication with the weight value is calculated, a total of20 bias values can be generated. Alternatively, the bias value can alsobe set for each weight.

When the weights and the bias of the respective links of the learningunits 160 and 360 are determined, and the five parameters are inputtedto the learning units 160 and 360, the values of 0, 1, or 2 are finallycalculated. The calculated value may be provided to the air conditioneras operation mode information that sets a load in a comfortable mode ofan air conditioner.

The set of weights applied to the links arranged from the input to thefirst hidden layer is {w1_1, w1_2, . . . , w1_i} and the set of bias is{b1_1, b1_2, . . . , b1_20}. Here, i may have a value of 20 or more and100 (5×20) or less. In FIG. 14, the link may be arranged between theinput node and all nodes of the first hidden layer, and ‘i’ may have avalue of 100. The values of twenty units of the first hidden layer maybe inputted by applying {w1_1, w1_2, . . . , w1_100} and {b1_1, b1_2, .. . , b1_20} to five inputted factors.

The set of weights applied to the links arranged from the first hiddenlayer to the second hidden layer is {w2_1, w2_2, . . . , w2_j}, and theset of bias is {b2_1, b2_2, . . . , b2_3}. Here, j can have a value of20 or more and 260 (20×13) or less. In FIG. 14, a link may be arrangedbetween the two nodes between the first hidden layer and the secondhidden layer, and j has a value of 260. {w2_1, w2_2, . . . , w2_260} and{b2_1, b2_2, . . . , b2_13} are applied to the values of 20 units of thefirst hidden layer and inputted to the values of thirteen units of thesecond hidden layer.

The set of weights applied to the links arranged from the second hiddenlayer to the third hidden layer is {w3_1, w3_2, . . . , w3_k}, and theset of bias is {b3_1, b3_2, . . . , b3_5}. Here, k can have a value ofat least 13 or more and 65 (5×13) or less. In FIG. 14, a link may bearranged between the two nodes between the second hidden layer and thethird hidden layer so that k has a value of 65. {w3_1, w3_2, . . . ,w3_65} and {b3_1, b3_2, . . . , b3_5} may be applied to the values ofthirteen units of the second hidden layer and inputted into the valuesof five units of the third hidden layer.

The set of weights applied to the link arranged from the third hiddenlayer to the output may be {w4_1, w4_2, . . . , w4_p}, and the set ofbias may be {b4_1, b4_2, . . . , b4_3}. Here, p can have a value of atleast 5 or more and 15 (5×3) or less. In FIG. 14, a link may be arrangedbetween the two nodes between the third hidden layer and the output, andp may have a value of 15. {w4_1, w4_2, . . . , w4_p} and {b4_1, b4_2, .. . , b4_3} are applied to the values of five units of the third hiddenlayer and are connected to three output units to determine the loaddegree.

FIG. 15 shows an interaction of the parameters of an air conditionerthat discharges cooling air and a provision of a wind speed and coolingair according to an implementation of the present disclosure. In a firstperiod to discharge the cooling air, the learning units 160 and 360 canincrease a wind speed in the following cases or an outdoor unit canincrease cooling air. In a state of i) proportional to an indoor initialtemperature (TempInit) or ii) inversely proportional to a target settemperature (TempTarget), or iii) inversely proportional to atemperature change rate (InitRate) in an initial time interval, or iv)inversely proportional to a temperature change rate (PowerRate) in afirst period, or v) proportional to a time magnitude (PowerTime), thelearning units 160 and 360 can output operation mode information thatincreases wind speed or controls an outdoor unit to increase cooling airand provide it.

Accordingly, when the learning units 160 and 360 may include one or morehidden layers, one or more edges and nodes that have high weight or theproportional weight is set between wind speed, air volume, or coolingair and TempInit/PowerTime can be arranged. A quadrangle expressed as“proportional” may shape such an edge and a node.

On the other hand, one or more edges and nodes that have the low weightor the inverse proportional weight is set may be arranged between thewind speed, the air volume, or the cooling air andTempTarget/InitRate/PowerRate. A quadrangle expressed as “inverseproportional” may shape such an edge and a node. FIG. 16 shows theinteraction of the parameters of air conditioner that discharges heatingair and a provision of heating air and the wind speed in accordance withanother implementation of the present disclosure.

The learning units 160 and 360 can increase a wind speed or an outdoorunit can increase heating air in a first period that discharges theheating air in the following cases. In the state of i) inverseproportional to an indoor initial temperature (TempInit), or ii)proportional to a target set temperature (TempTarget), or iii) inverseproportional to a temperature change rate (InitRate) in an initial timeinterval, or iv) inverse proportional to a temperature change rate(PowerRate) in a first period, or v) proportional to a time magnitude(PowerTime), the learning units 160 and 360 can output operation modeinformation that increases wind speed or controls an outdoor unit toincrease cooling air and provide it.

Thus, the learning units 160 and 360 are made of one or more hiddenlayers, one or more of edges and nodes that have the high weight orproportional weight is set may be arranged between a wind speed, airvolume, cooling air, and TempTarget/PowerTime. A quadrangle indicated by“proportional” may shape such an edge and a node.

On the other hand, one or more of edges and nodes that have the lowweight and inversely proportional weight is set may be arranged betweenthe wind speed, air volume, or cooling air andTempInit/InitRate/PowerRate. A quadrangle indicated by “inverselyproportional” may shape such an edge and a node.

The proportion/inverse proportion and the parameters shown in FIG. 15and FIG. 16 are merely the implementation and can be selected in variousways.

As shown in FIGS. 13 to 16, the learning units 160 and 360 may receiveone or more parameters as a learning factor, and when the learningfactors are different, the outputted operation mode information may bedifferent. For example, when the first learning factor inputted at thefirst time point and the second learning factor inputted at the secondtime point after the first time point are different from each other, thelearning units 160 and 360 may obtain the second operation modeinformation different from the first operation mode informationoutputted at the first time point so that the operation mode informationon the second period is outputted differently in the first time pointand the second time point. As a result, the operation mode informationon the second period at the first time point and the operation modeinformation on the second period at the second time point can bedifferent from each other.

According to implementations of the present disclosure being applied, itis possible to provide a method of estimating learning based load forthe efficient cooling or heating control of the air conditioner and anapparatus applying such method. Particularly, according to theimplementations of the present disclosure, the correlation between anenvironmental factor before/after the two points of reaching the targetset temperature and a temperature pattern according to cooling (orheating) is learned to estimate load in a stepwise so that an efficientcooling operation is possible after reaching a target temperature. Thus,even if the same target temperature is set, various environmentalfactors until the temperature reaches the target temperature may be anelement that differently controls an operation of the air conditionerafter reaching the target temperature.

When the implementations of the present disclosure are applied, the airconditioner may have the parameter generated during operation as thelearning factor to determine the operation mode with respect thereto.

When the implementations of the present disclosure are applied, thecloud server can determine the operation mode suitable for each airconditioner after learning based on the parameters that the plurality ofair conditioners determine and provide in the operation process.

When the implementations of the present disclosure are applied, afterthe air conditioner is operated to reach the predetermined targettemperature, it is possible to maintain the target temperature withinthe predetermined range based on a small amount of consumptionelectrical power per unit time.

In the implementation of the present disclosure, a consumptionelectrical power per unit time of the second period (the comfortablemode) is less than a consumption electrical power per unit time of thefirst period (the rapid mode). In the first period, the electrical poweris used at most for rapid cooling/heating initially, and the time isshort. On the other hand, in the second period, a level of thecooling/heating provided in the first period is maintained and the timeis long. Therefore, the consumption electrical power per unit time ofthe first period is greater than the consumption electrical power perunit time of the second period. Of course, the temporal magnitude in thefirst period may also be less than the temporal magnitude in the secondperiod. For example, the first period may be set not to exceed a maximumof 10 minutes, but the second period may be maintained for a longerperiod of 3 hours or 5 hours.

The second period may be stopped at a time point at which the indoorunit stops providing the cooling or heating function, for example, at atime point at which the user changes an operation additionally such asconverting the mode or turning off a power, and the like. Therefore, theelectrical power consumption at the beginning of the second period maybe equal to or greater than the electrical power consumption in thefirst period. However, when an average consumption electrical power perunit time (K2) is calculated based on the total time of the secondperiod, it satisfies K2<K1, in comparison with an average consumptionelectrical power per unit time (K1) used in the first period.

Although components included in the implementation of the presentdisclosure are described as being combined to one, or as being operatedto operate, the present disclosure is not necessarily limited to such animplementation, and these components may operate by being selectivelycombined to one or more within the purpose range of the presentdisclosure. Further, although all of the components may be implementedas an independent hardware, a part or all of each of the components maybe selectively combined to be implemented as a computer program that hasa program module that performs a part or all of the function combined inone or a plurality of hardwares. The codes and the code segments thatform the computer program will be easily deduced by those skilled in theart of the present disclosure. Such a computer program can be stored ina computer readable media that a computer can read, and can be read andimplemented by the computer to implement the implementation of thepresent disclosure. As the storage medium of the computer program, itmay include a storage media including a semiconductor recording element,an optical recording media, and a magnetic recording media. Further, acomputer program that implements the implementation of the presentdisclosure may include a program module that is transmitted in real timevia an external apparatus.

What is claimed:
 1. An air conditioner, comprising: at least oneprocessor; and at least one computer memory operably connectable to theat least one processor and storing instructions that, when executed bythe at least one processor, perform operations comprising: generating atleast one parameter in a first operation mode of the air conditioner,wherein the first operation mode is operated only within a preset timerange; controlling, based on operation mode information including atleast one result factor that is obtained from at least onemachine-learning network, at least one of (i) a wind direction and anair volume of discharged air, or (ii) supply of compressed refrigerantfor exchanging heat with air in the air conditioner; and performing,based on the generated at least one parameter, an operation of switchingto a second operation mode after a time period of operation in the firstoperation mode, wherein an absolute value of a first temperature changerate during the first operation mode is greater than an absolute valueof a second temperature change rate during the second operation mode,wherein the air conditioner is configured to change a room temperaturein a stepwise change rate after the first operation mode is terminated,wherein, (i) based on a difference between a current temperature and atarget set temperature being within a temperature band or (ii) based onthe difference being maintained for a time period in the secondoperation mode, the operations further comprise performing an operationto switch to the first operation mode, and wherein the target settemperature is automatically set to a temperature that is determined byan external server based on big data in response to the currenttemperature.
 2. The air conditioner of claim 1, wherein the airconditioner is configured to communicate with a cloud server, whereinthe at least one result factor obtained from the at least onemachine-learning network is obtained from at least one of (i) the cloudserver after the air conditioner transmits the at least one parameter tothe cloud server, or (ii) an embedded learning unit.
 3. The airconditioner of claim 1, wherein the operations further comprise: basedon the air conditioner operating for cooling, maintaining a temperatureduring a first time interval from a time point when the first operationmode ends, and subsequently increasing the temperature compared to thetemperature of the first time interval during a second time interval;and based on the air conditioner operating for heating, maintaining atemperature during a first time interval from a time point when thefirst operation mode ends, and subsequently reducing the temperaturecompared to the temperature of the first time interval during a secondtime interval, wherein the first time interval and the second timeinterval form a time interval of the second operation mode.
 4. The airconditioner of claim 3, wherein the operations further comprise:receiving, through an interface unit, a control signal related to anincrease or a decrease in a temperature of the air conditioner, wherein,based on the air conditioner operating for cooling in the secondoperation mode, increasing or decreasing a length of the first timeinterval or the second time interval in inverse proportion to theincrease or the decrease of the temperature by the control signal. 5.The air conditioner of claim 1, wherein the operations further comprise:performing a humidity control operation mode based on a humidity level,which is sensed at a time of switching from the second operation mode tothe humidity control operation mode, exceeding a first referencethreshold criteria, and releasing the humidity control operation modebased on the humidity level reaching a second reference thresholdcriteria and being maintained for a period of time.
 6. The airconditioner of claim 1, wherein the operations further comprise: storingposition information related to a residing area, based on an occupantrepeatedly being detected in a space where the air conditioner isinstalled; and setting a left or right wind direction in the firstoperation mode to be directed to the residing area.
 7. The airconditioner of claim 6, wherein the operations further comprise: storingposition information related to an activity area that the residing area,based on the occupant being intermittently detected; and setting theleft or right wind direction in the second operation mode to theactivity area.
 8. The air conditioner of claim 1, wherein the operationsfurther comprise: based on (i) the air conditioner starting to operateand a difference between a current indoor temperature and the target settemperature being greater than a predefined value, or (ii) the airconditioner receiving an input signal that instructs a driving of thefirst operation mode: performing an operation of switching to the firstoperation mode.
 9. The air conditioner of claim 1, wherein the at leastone parameter comprises: (i) an indoor initial temperature related to atime at which the first operation mode is started, (ii) a target settemperature for the first operation mode, (iii) a temperature changerate during an initial time interval of the first operation mode, (iv) atemperature change rate during the first operation mode, and (v) a timedifference between a start time and an end time of the first operationmode.
 10. The air conditioner of claim 6, wherein the at least onemachine-learning network comprises: at least one input layer that hasthe at least one parameter as at least one input node; at least oneoutput layer that has the operation mode information as at least oneoutput node; and at least one hidden layer arranged between the at leastone input layer and the at least one output layer, wherein weights of atleast one node and at least one edge between the at least one input nodeand the at least one output node are updated by a learning process ofthe at least one machine-learning network.
 11. A method of controllingan air conditioner based on machine-learning, the method comprising:discharging air in a first operation mode of the air conditioner withina preset time range, wherein the first operation mode is operated onlywithin a preset time range; generating at least one parameter in thefirst operation mode of the air conditioner; controlling, based onoperation mode information including at least one result factor that isobtained from at least one machine-learning network, at least one of (i)a wind direction and an air volume of discharged air, or (ii) supply ofcompressed refrigerant for exchanging heat with air in the airconditioner; and performing, based on the generated at least oneparameter, an operation of switching to a second operation mode after atime period of operation in the first operation mode, wherein anabsolute value of a first temperature change rate during the firstoperation mode is greater than an absolute value of a second temperaturechange rate during the second operation mode, wherein the airconditioner is configured to change a room temperature in a stepwisechange rate after the first operation mode is terminated, wherein, (i)based on a difference between a current temperature and a target settemperature being within a temperature band or (ii) based on thedifference being maintained for a time period in the second operationmode, the operations further comprise performing an operation to switchto the first operation mode, and wherein the target set temperature isautomatically set to a temperature that is determined by an externalserver based on big data in response to the current temperature.
 12. Themethod of claim 11, further comprising: based on the air conditioneroperating for cooling, maintaining a temperature during a first timeinterval from a time point when the first operation mode ends, andsubsequently increasing the temperature compared to the temperature ofthe first time interval during a second time interval; and based on theair conditioner operating for heating, maintaining a temperature duringa first time interval from a time point when the first operation modeends, and subsequently reducing the temperature compared to thetemperature of the first time interval during a second time interval,wherein the first time interval and the second time interval form a timeinterval of the second operation mode.
 13. The method of claim 12,wherein the at least one parameter comprises (i) an indoor initialtemperature related to a time at which the first operation mode isstarted, (ii) a target set temperature for the first operation mode,(iii) a temperature change rate during an initial time interval of thefirst operation mode, (iv) a temperature change rate during the firstoperation mode, and (v) a time difference between a start time and anend time of the first operation mode.
 14. The method of claim 11,further comprising: performing a humidity control operation mode basedon a humidity level, which is sensed at a time of switching from thefirst operation mode to the second operation mode, exceeding a firstreference threshold criteria, and releasing the humidity controloperation mode based on the humidity level reaching a second referencethreshold criteria and being maintained for a period of time.
 15. Acloud server, comprising: at least one processor; and at least onecomputer memory operably connectable to the at least one processor andstoring instructions that, when executed by the at least one processor,perform operations comprising: receiving, from each of plurality of airconditioners, at least one parameter generated in a first operation modeof respective air conditioner, wherein the at least one parameter isrelated to at least one temperature individually set in the respectiveair conditioner; for each of the plurality of air conditioners, using atleast one machine-learning network to process the at least one parameterreceived from the respective air conditioner, and generating, through alearning process, operation mode information for the respective airconditioner; and transmitting, to each of the plurality of airconditioners, the operation mode information for the respective airconditioner, wherein the cloud server communicates with the plurality ofair conditioners, wherein the operation mode information comprises datato set a second operation mode of the respective air conditioner after aperiod of time of operating in the first operation mode based on thereceived at least one parameter, wherein an absolute value of a firsttemperature change rate during the first operation mode is greater thanan absolute value of a second temperature change rate during the secondoperation mode of the respective air conditioner, wherein the respectiveair conditioner is configured to change a room temperature in a stepwisechange rate after the first operation mode is terminated, wherein, (i)based on a difference between a current temperature and a target settemperature being within a temperature band or (ii) based on thedifference being maintained for a time period in the second operationmode, the operations further comprise performing an operation to switchto the first operation mode, and wherein the target set temperature isautomatically set to a temperature that is determined by an externalserver based on big data in response to the current temperature.
 16. Thecloud server of claim 15, wherein, for each of the plurality of airconditioners, the at least one parameter comprises (i) an indoor initialtemperature related to a time at which the first operation mode isstarted for the respective air conditioner, (ii), target set temperaturefor the first operation mode of the respective air conditioner, (iii) atemperature change rate during an initial time interval of the firstoperation mode of the respective air conditioner, (iv) a temperaturechange rate during the first operation mode of the respective airconditioner, and (v) a time difference between a start time and an endtime of the first operation mode of the respective air conditioner. 17.The cloud server of claim 16, wherein the at least one machine-learningnetwork comprises: at least one input layer that has the at least oneparameter as at least one input node; at least one output layer that hasthe operation mode information as at least one output node; and at leastone hidden layer arranged between the at least one input layer and theat east one output layer, wherein weights of at least one node and atleast one edge of the at least one input node and the at least oneoutput node are updated by a learning process of the at least onemachine-learning network.
 18. The cloud server of claim 15, whereinusing at least one machine-learning network and generating the operationmode information further comprises: inputting the received at least oneparameter to the at least one machine-learning network that uses deeplearning to calculate at least one result factor, the at least oneresult factor including information regarding an operation mode; andgenerating the operation mode information based on the at least oneresult factor.